Daily Dose of Data Science | Avi Chawla | Substack·2y6 Elegant Jupyter Hacks
Discover 6 elegant Jupyter hacks to improve your experience. Learn how to retrieve a cell's output, enrich the default preview of a DataFrame, generate helpful hints as you write Pandas code, improve rendering of DataFrames, restart the Jupyter kernel without losing variables, and search code in all Jupyter Notebooks from the terminal.
Python in Plain English·2yCreating an ETL Data Pipeline Using Bash with Apache Airflow
Learn how to create an ETL data pipeline using bash with Apache Airflow. Extract data from various file formats, transform it, and load it into a new file. Includes steps for starting Apache Airflow, downloading the dataset, creating a DAG, and executing the pipeline.
Real Python·2yHow to Create Pivot Tables With pandas – Real Python
Learn how to create pivot tables with pandas in Python. Pivot tables are a data analysis tool used to summarize and analyze data. The tutorial covers the basics of creating pivot tables, including grouping, aggregating, and formatting the data.
KDnuggets·2y3 Courses You Should Consider If You Want to Become a Data Analyst
This post discusses three different courses that individuals can consider taking if they want to become a data analyst. It highlights courses offered by DataCamp, Meta, and Google, providing information on the skills and knowledge that can be gained from each.
Hacker News·2yquarylabs/quary: Open-source BI for engineers
Quary is an open-source BI tool for engineers that allows you to connect to databases, write SQL queries to transform and organize data, create charts and dashboards, and deploy the model back to the database. It can be installed as a VSCode extension or a Rust-based CLI.
DevBlogs·2yAnnouncing Data Wrangler: Code-centric viewing and cleaning of tabular data in Visual Studio Code
Announcing Data Wrangler: Code-centric viewing and cleaning of tabular data in Visual Studio Code. Data Wrangler is a free extension that offers data viewing and cleaning directly integrated into VS Code and the Jupyter extension. It provides a rich user interface for data analysis and transformation, with features such as column statistics, visualizations, and automatic code generation.
KDnuggets·2yFeature Engineering for Beginners
This guide introduces key techniques in feature engineering, including handling missing values, encoding categorical variables, and scaling and normalizing data. It also covers advanced techniques such as feature creation, dimensionality reduction, and time series feature engineering. The post provides practical examples in Python and offers practical tips and best practices.
Substack·2yHow to Build a Data Analytics Portfolio
Learn how to build a data analytics portfolio by including projects that showcase skills like exploratory data analysis, data visualization, and business acumen. Use GitHub to organize your projects and create a comprehensive ReadMe page to highlight your accomplishments, methodology, and business recommendations.
freeCodeCamp·2yData Analysis with Python – How I Analyzed My Empire State Building Run-Up Performance
The post discusses the author's experience participating in the Empire State Building Run-Up and their subsequent analysis of their performance using data analysis with Python. The author describes the challenges in obtaining and analyzing race data, and highlights the use of open source tools to retrieve and analyze the data.
TigerData (Creators of TimescaleDB)·2yPostgreSQL Data Cleaning vs. Python Data Cleaning
This post explores how PostgreSQL and TimescaleDB can be used for efficient data cleaning tasks, replacing the need for tools like Excel, R, or Python. By cleaning data directly within the database, tasks can be performed more efficiently, saving time in the long run.
GoPenAI·2yCustomer Lifetime Value (CLV) Prediction With Machine Learning and DB Querying With LLM
This post discusses the prediction of Customer Lifetime Value (CLV) for auto insurance clients using machine learning and database querying with LLM. It covers data extraction and cleaning, exploratory data analysis, machine learning model selection and optimization, user interface for CLV prediction, and a Q&A interface for data retrieval. The project aims to improve customer retention, enhance marketing effectiveness, facilitate data-driven decision-making, and provide a user-friendly experience.
